diff --git a/.github/workflows/test.yaml b/.github/workflows/test.yaml
index 6bd859d2..20946c19 100644
--- a/.github/workflows/test.yaml
+++ b/.github/workflows/test.yaml
@@ -4,7 +4,7 @@ on:
   workflow_dispatch:
   push:
     branches:
-      - 'main'
+      - "main"
   pull_request:
     paths:
       - ".github/workflows/build.yaml"
@@ -17,7 +17,7 @@ on:
       - "rust-toolchain.toml"
       - "Dockerfile"
     branches:
-      - 'main'
+      - "main"
 
 jobs:
   tests:
@@ -38,6 +38,7 @@ jobs:
         env:
           SCCACHE_GHA_ENABLED: "true"
           RUSTC_WRAPPER: "sccache"
+          HF_TOKEN: ${{ secrets.HF_HUB_READ_TOKEN }}
         run: |
           sudo apt-get update && sudo apt-get install protobuf-compiler -y
           cargo test --profile=release-debug
diff --git a/README.md b/README.md
index f2508852..c27a4bc5 100644
--- a/README.md
+++ b/README.md
@@ -11,7 +11,7 @@
 
 A blazing fast inference solution for text embeddings models.
 
-Benchmark for [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) on an Nvidia A10 with a sequence
+Benchmark for [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) on an NVIDIA A10 with a sequence
 length of 512 tokens:
 
 <p>
@@ -66,29 +66,31 @@ Ember, GTE and E5. TEI implements many features such as:
 #### Text Embeddings
 
 Text Embeddings Inference currently supports Nomic, BERT, CamemBERT, XLM-RoBERTa models with absolute positions, JinaBERT
-model with Alibi positions and Mistral, Alibaba GTE, Qwen2 models with Rope positions, MPNet, ModernBERT, and Qwen3.
+model with Alibi positions and Mistral, Alibaba GTE, Qwen2 models with Rope positions, MPNet, ModernBERT, Qwen3, and Gemma3.
 
 Below are some examples of the currently supported models:
 
-| MTEB Rank | Model Size          | Model Type  | Model ID                                                                                         |
-|-----------|---------------------|-------------|--------------------------------------------------------------------------------------------------|
-| 2         | 8B (Very Expensive) | Qwen3       | [Qwen/Qwen3-Embedding-8B](https://hf.co/Qwen/Qwen3-Embedding-8B)                                 |
-| 4         | 0.6B                | Qwen3       | [Qwen/Qwen3-Embedding-0.6B](https://hf.co/Qwen/Qwen3-Embedding-0.6B)                             |
-| 6         | 7B (Very Expensive) | Qwen2       | [Alibaba-NLP/gte-Qwen2-7B-instruct](https://hf.co/Alibaba-NLP/gte-Qwen2-7B-instruct)             |
-| 7         | 0.5B                | XLM-RoBERTa | [intfloat/multilingual-e5-large-instruct](https://hf.co/intfloat/multilingual-e5-large-instruct) |
-| 14        | 1.5B (Expensive)    | Qwen2       | [Alibaba-NLP/gte-Qwen2-1.5B-instruct](https://hf.co/Alibaba-NLP/gte-Qwen2-1.5B-instruct)         |
-| 17        | 7B (Very Expensive) | Mistral     | [Salesforce/SFR-Embedding-2_R](https://hf.co/Salesforce/SFR-Embedding-2_R)                       |
-| 34        | 0.5B                | XLM-RoBERTa | [Snowflake/snowflake-arctic-embed-l-v2.0](https://hf.co/Snowflake/snowflake-arctic-embed-l-v2.0) |
-| 40        | 0.3B                | Alibaba GTE | [Snowflake/snowflake-arctic-embed-m-v2.0](https://hf.co/Snowflake/snowflake-arctic-embed-m-v2.0) |
-| 51        | 0.3B                | Bert        | [WhereIsAI/UAE-Large-V1](https://hf.co/WhereIsAI/UAE-Large-V1)                                   |
-| N/A       | 0.4B                | Alibaba GTE | [Alibaba-NLP/gte-large-en-v1.5](https://hf.co/Alibaba-NLP/gte-large-en-v1.5)                     |
-| N/A       | 0.4B                | ModernBERT  | [answerdotai/ModernBERT-large](https://hf.co/answerdotai/ModernBERT-large)                       |
-| N/A       | 0.3B                | NomicBert   | [nomic-ai/nomic-embed-text-v2-moe](https://hf.co/nomic-ai/nomic-embed-text-v2-moe)               |
-| N/A       | 0.1B                | NomicBert   | [nomic-ai/nomic-embed-text-v1](https://hf.co/nomic-ai/nomic-embed-text-v1)                       |
-| N/A       | 0.1B                | NomicBert   | [nomic-ai/nomic-embed-text-v1.5](https://hf.co/nomic-ai/nomic-embed-text-v1.5)                   |
-| N/A       | 0.1B                | JinaBERT    | [jinaai/jina-embeddings-v2-base-en](https://hf.co/jinaai/jina-embeddings-v2-base-en)             |
-| N/A       | 0.1B                | JinaBERT    | [jinaai/jina-embeddings-v2-base-code](https://hf.co/jinaai/jina-embeddings-v2-base-code)         |
-| N/A       | 0.1B                | MPNet       | [sentence-transformers/all-mpnet-base-v2](https://hf.co/sentence-transformers/all-mpnet-base-v2) |
+| MTEB Rank | Model Size             | Model Type     | Model ID                                                                                         |
+|-----------|------------------------|----------------|--------------------------------------------------------------------------------------------------|
+| 2         | 7.57B (Very Expensive) | Qwen3          | [Qwen/Qwen3-Embedding-8B](https://hf.co/Qwen/Qwen3-Embedding-8B)                                 |
+| 3         | 4.02B (Very Expensive) | Qwen3          | [Qwen/Qwen3-Embedding-4B](https://hf.co/Qwen/Qwen3-Embedding-4B)                                 |
+| 4         | 509M                   | Qwen3          | [Qwen/Qwen3-Embedding-0.6B](https://hf.co/Qwen/Qwen3-Embedding-0.6B)                             |
+| 6         | 7.61B (Very Expensive) | Qwen2          | [Alibaba-NLP/gte-Qwen2-7B-instruct](https://hf.co/Alibaba-NLP/gte-Qwen2-7B-instruct)             |
+| 7         | 560M                   | XLM-RoBERTa    | [intfloat/multilingual-e5-large-instruct](https://hf.co/intfloat/multilingual-e5-large-instruct) |
+| 8         | 308M                   | Gemma3         | [google/embeddinggemma-300m](https://hf.co/google/embeddinggemma-300m) (gated)                   |
+| 15        | 1.78B (Expensive)      | Qwen2          | [Alibaba-NLP/gte-Qwen2-1.5B-instruct](https://hf.co/Alibaba-NLP/gte-Qwen2-1.5B-instruct)         |
+| 18        | 7.11B (Very Expensive) | Mistral        | [Salesforce/SFR-Embedding-2_R](https://hf.co/Salesforce/SFR-Embedding-2_R)                       |
+| 35        | 568M                   | XLM-RoBERTa    | [Snowflake/snowflake-arctic-embed-l-v2.0](https://hf.co/Snowflake/snowflake-arctic-embed-l-v2.0) |
+| 41        | 305M                   | Alibaba GTE    | [Snowflake/snowflake-arctic-embed-m-v2.0](https://hf.co/Snowflake/snowflake-arctic-embed-m-v2.0) |
+| 52        | 335M                   | BERT           | [WhereIsAI/UAE-Large-V1](https://hf.co/WhereIsAI/UAE-Large-V1)                                   |
+| 58        | 137M                   | NomicBERT      | [nomic-ai/nomic-embed-text-v1](https://hf.co/nomic-ai/nomic-embed-text-v1)                       |
+| 79        | 137M                   | NomicBERT      | [nomic-ai/nomic-embed-text-v1.5](https://hf.co/nomic-ai/nomic-embed-text-v1.5)                   |
+| 103       | 109M                   | MPNet          | [sentence-transformers/all-mpnet-base-v2](https://hf.co/sentence-transformers/all-mpnet-base-v2) |
+| N/A       | 475M-A305M             | NomicBERT      | [nomic-ai/nomic-embed-text-v2-moe](https://hf.co/nomic-ai/nomic-embed-text-v2-moe)               |
+| N/A       | 434M                   | Alibaba GTE    | [Alibaba-NLP/gte-large-en-v1.5](https://hf.co/Alibaba-NLP/gte-large-en-v1.5)                     |
+| N/A       | 396M                   | ModernBERT     | [answerdotai/ModernBERT-large](https://hf.co/answerdotai/ModernBERT-large)                       |
+| N/A       | 137M                   | JinaBERT       | [jinaai/jina-embeddings-v2-base-en](https://hf.co/jinaai/jina-embeddings-v2-base-en)             |
+| N/A       | 137M                   | JinaBERT       | [jinaai/jina-embeddings-v2-base-code](https://hf.co/jinaai/jina-embeddings-v2-base-code)         |
 
 To explore the list of best performing text embeddings models, visit the
 [Massive Text Embedding Benchmark (MTEB) Leaderboard](https://huggingface.co/spaces/mteb/leaderboard).
@@ -352,15 +354,15 @@ You have the option to utilize the `HF_TOKEN` environment variable for configuri
 For example:
 
 1. Go to https://huggingface.co/settings/tokens
-2. Copy your cli READ token
-3. Export `HF_TOKEN=<your cli READ token>`
+2. Copy your CLI READ token
+3. Export `HF_TOKEN=<your CLI READ token>`
 
 or with Docker:
 
 ```shell
 model=<your private model>
 volume=$PWD/data # share a volume with the Docker container to avoid downloading weights every run
-token=<your cli READ token>
+token=<your CLI READ token>
 
 docker run --gpus all -e HF_TOKEN=$token -p 8080:80 -v $volume:/data --pull always ghcr.io/huggingface/text-embeddings-inference:1.8 --model-id $model
 ```
diff --git a/backends/candle/tests/snapshots/test_gemma3__gemma3_cpu_batch.snap b/backends/candle/tests/snapshots/test_gemma3__gemma3_cpu_batch.snap
new file mode 100644
index 00000000..12534fac
--- /dev/null
+++ b/backends/candle/tests/snapshots/test_gemma3__gemma3_cpu_batch.snap
@@ -0,0 +1,2308 @@
+---
+source: backends/candle/tests/test_gemma3.rs
+expression: embeddings_batch
+---
+- - -69.487144
+  - -7.1062355
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diff --git a/backends/candle/tests/snapshots/test_gemma3__gemma3_cpu_single.snap b/backends/candle/tests/snapshots/test_gemma3__gemma3_cpu_single.snap
new file mode 100644
index 00000000..198c9c11
--- /dev/null
+++ b/backends/candle/tests/snapshots/test_gemma3__gemma3_cpu_single.snap
@@ -0,0 +1,772 @@
+---
+source: backends/candle/tests/test_gemma3.rs
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diff --git a/backends/candle/tests/test_gemma3.rs b/backends/candle/tests/test_gemma3.rs
new file mode 100644
index 00000000..bff702ce
--- /dev/null
+++ b/backends/candle/tests/test_gemma3.rs
@@ -0,0 +1,52 @@
+mod common;
+
+use crate::common::{sort_embeddings, SnapshotEmbeddings};
+use anyhow::Result;
+use common::{batch, cosine_matcher, download_artifacts, load_tokenizer};
+use text_embeddings_backend_candle::CandleBackend;
+use text_embeddings_backend_core::{Backend, ModelType, Pool};
+
+#[test]
+#[serial_test::serial]
+fn test_gemma3() -> Result<()> {
+    let (model_root, dense_paths) = download_artifacts("google/embeddinggemma-300m", None, None)?;
+    let tokenizer = load_tokenizer(&model_root)?;
+
+    let backend = CandleBackend::new(
+        &model_root,
+        "float32".to_string(),
+        ModelType::Embedding(Pool::Mean),
+        dense_paths,
+    )?;
+
+    let input_batch = batch(
+        vec![
+            tokenizer.encode("What is Deep Learning?", true).unwrap(),
+            tokenizer.encode("Deep Learning is...", true).unwrap(),
+            tokenizer.encode("What is Deep Learning?", true).unwrap(),
+        ],
+        [0, 1, 2].to_vec(),
+        vec![],
+    );
+
+    let matcher = cosine_matcher();
+
+    let (pooled_embeddings, _) = sort_embeddings(backend.embed(input_batch)?);
+    let embeddings_batch = SnapshotEmbeddings::from(pooled_embeddings);
+    insta::assert_yaml_snapshot!("gemma3_cpu_batch", embeddings_batch, &matcher);
+
+    let input_single = batch(
+        vec![tokenizer.encode("What is Deep Learning?", true).unwrap()],
+        [0].to_vec(),
+        vec![],
+    );
+
+    let (pooled_embeddings, _) = sort_embeddings(backend.embed(input_single)?);
+    let embeddings_single = SnapshotEmbeddings::from(pooled_embeddings);
+
+    insta::assert_yaml_snapshot!("gemma3_cpu_single", embeddings_single, &matcher);
+    assert_eq!(embeddings_batch[0], embeddings_single[0]);
+    assert_eq!(embeddings_batch[2], embeddings_single[0]);
+
+    Ok(())
+}
diff --git a/docs/source/en/supported_models.md b/docs/source/en/supported_models.md
index a4d82fd8..d7690603 100644
--- a/docs/source/en/supported_models.md
+++ b/docs/source/en/supported_models.md
@@ -21,29 +21,31 @@ We are continually expanding our support for other model types and plan to inclu
 ## Supported embeddings models
 
 Text Embeddings Inference currently supports Nomic, BERT, CamemBERT, XLM-RoBERTa models with absolute positions, JinaBERT
-model with Alibi positions and Mistral, Alibaba GTE, Qwen2 models with Rope positions, MPNet, ModernBERT, and Qwen3.
+model with Alibi positions and Mistral, Alibaba GTE, Qwen2 models with Rope positions, MPNet, ModernBERT, Qwen3, and Gemma3.
 
 Below are some examples of the currently supported models:
 
-| MTEB Rank | Model Size          | Model Type  | Model ID                                                                                         |
-|-----------|---------------------|-------------|--------------------------------------------------------------------------------------------------|
-| 2         | 8B (Very Expensive) | Qwen3       | [Qwen/Qwen3-Embedding-8B](https://hf.co/Qwen/Qwen3-Embedding-8B)                                 |
-| 4         | 0.6B                | Qwen3       | [Qwen/Qwen3-Embedding-0.6B](https://hf.co/Qwen/Qwen3-Embedding-0.6B)                             |
-| 6         | 7B (Very Expensive) | Qwen2       | [Alibaba-NLP/gte-Qwen2-7B-instruct](https://hf.co/Alibaba-NLP/gte-Qwen2-7B-instruct)             |
-| 7         | 0.5B                | XLM-RoBERTa | [intfloat/multilingual-e5-large-instruct](https://hf.co/intfloat/multilingual-e5-large-instruct) |
-| 14        | 1.5B (Expensive)    | Qwen2       | [Alibaba-NLP/gte-Qwen2-1.5B-instruct](https://hf.co/Alibaba-NLP/gte-Qwen2-1.5B-instruct)         |
-| 17        | 7B (Very Expensive) | Mistral     | [Salesforce/SFR-Embedding-2_R](https://hf.co/Salesforce/SFR-Embedding-2_R)                       |
-| 34        | 0.5B                | XLM-RoBERTa | [Snowflake/snowflake-arctic-embed-l-v2.0](https://hf.co/Snowflake/snowflake-arctic-embed-l-v2.0) |
-| 40        | 0.3B                | Alibaba GTE | [Snowflake/snowflake-arctic-embed-m-v2.0](https://hf.co/Snowflake/snowflake-arctic-embed-m-v2.0) |
-| 51        | 0.3B                | Bert        | [WhereIsAI/UAE-Large-V1](https://hf.co/WhereIsAI/UAE-Large-V1)                                   |
-| N/A       | 0.4B                | Alibaba GTE | [Alibaba-NLP/gte-large-en-v1.5](https://hf.co/Alibaba-NLP/gte-large-en-v1.5)                     |
-| N/A       | 0.4B                | ModernBERT  | [answerdotai/ModernBERT-large](https://hf.co/answerdotai/ModernBERT-large)                       |
-| N/A       | 0.3B                | NomicBert   | [nomic-ai/nomic-embed-text-v2-moe](https://hf.co/nomic-ai/nomic-embed-text-v2-moe)               |
-| N/A       | 0.1B                | NomicBert   | [nomic-ai/nomic-embed-text-v1](https://hf.co/nomic-ai/nomic-embed-text-v1)                       |
-| N/A       | 0.1B                | NomicBert   | [nomic-ai/nomic-embed-text-v1.5](https://hf.co/nomic-ai/nomic-embed-text-v1.5)                   |
-| N/A       | 0.1B                | JinaBERT    | [jinaai/jina-embeddings-v2-base-en](https://hf.co/jinaai/jina-embeddings-v2-base-en)             |
-| N/A       | 0.1B                | JinaBERT    | [jinaai/jina-embeddings-v2-base-code](https://hf.co/jinaai/jina-embeddings-v2-base-code)         |
-| N/A       | 0.1B                | MPNet       | [sentence-transformers/all-mpnet-base-v2](https://hf.co/sentence-transformers/all-mpnet-base-v2) |
+| MTEB Rank | Model Size             | Model Type     | Model ID                                                                                         |
+|-----------|------------------------|----------------|--------------------------------------------------------------------------------------------------|
+| 2         | 7.57B (Very Expensive) | Qwen3          | [Qwen/Qwen3-Embedding-8B](https://hf.co/Qwen/Qwen3-Embedding-8B)                                 |
+| 3         | 4.02B (Very Expensive) | Qwen3          | [Qwen/Qwen3-Embedding-4B](https://hf.co/Qwen/Qwen3-Embedding-4B)                                 |
+| 4         | 509M                   | Qwen3          | [Qwen/Qwen3-Embedding-0.6B](https://hf.co/Qwen/Qwen3-Embedding-0.6B)                             |
+| 6         | 7.61B (Very Expensive) | Qwen2          | [Alibaba-NLP/gte-Qwen2-7B-instruct](https://hf.co/Alibaba-NLP/gte-Qwen2-7B-instruct)             |
+| 7         | 560M                   | XLM-RoBERTa    | [intfloat/multilingual-e5-large-instruct](https://hf.co/intfloat/multilingual-e5-large-instruct) |
+| 8         | 308M                   | Gemma3         | [google/embeddinggemma-300m](https://hf.co/google/embeddinggemma-300m) (gated)                   |
+| 15        | 1.78B (Expensive)      | Qwen2          | [Alibaba-NLP/gte-Qwen2-1.5B-instruct](https://hf.co/Alibaba-NLP/gte-Qwen2-1.5B-instruct)         |
+| 18        | 7.11B (Very Expensive) | Mistral        | [Salesforce/SFR-Embedding-2_R](https://hf.co/Salesforce/SFR-Embedding-2_R)                       |
+| 35        | 568M                   | XLM-RoBERTa    | [Snowflake/snowflake-arctic-embed-l-v2.0](https://hf.co/Snowflake/snowflake-arctic-embed-l-v2.0) |
+| 41        | 305M                   | Alibaba GTE    | [Snowflake/snowflake-arctic-embed-m-v2.0](https://hf.co/Snowflake/snowflake-arctic-embed-m-v2.0) |
+| 52        | 335M                   | BERT           | [WhereIsAI/UAE-Large-V1](https://hf.co/WhereIsAI/UAE-Large-V1)                                   |
+| 58        | 137M                   | NomicBERT      | [nomic-ai/nomic-embed-text-v1](https://hf.co/nomic-ai/nomic-embed-text-v1)                       |
+| 79        | 137M                   | NomicBERT      | [nomic-ai/nomic-embed-text-v1.5](https://hf.co/nomic-ai/nomic-embed-text-v1.5)                   |
+| 103       | 109M                   | MPNet          | [sentence-transformers/all-mpnet-base-v2](https://hf.co/sentence-transformers/all-mpnet-base-v2) |
+| N/A       | 475M-A305M             | NomicBERT      | [nomic-ai/nomic-embed-text-v2-moe](https://hf.co/nomic-ai/nomic-embed-text-v2-moe)               |
+| N/A       | 434M                   | Alibaba GTE    | [Alibaba-NLP/gte-large-en-v1.5](https://hf.co/Alibaba-NLP/gte-large-en-v1.5)                     |
+| N/A       | 396M                   | ModernBERT     | [answerdotai/ModernBERT-large](https://hf.co/answerdotai/ModernBERT-large)                       |
+| N/A       | 137M                   | JinaBERT       | [jinaai/jina-embeddings-v2-base-en](https://hf.co/jinaai/jina-embeddings-v2-base-en)             |
+| N/A       | 137M                   | JinaBERT       | [jinaai/jina-embeddings-v2-base-code](https://hf.co/jinaai/jina-embeddings-v2-base-code)         |
 
 To explore the list of best performing text embeddings models, visit the
 [Massive Text Embedding Benchmark (MTEB) Leaderboard](https://huggingface.co/spaces/mteb/leaderboard).